FastVLM 7B — Hardware Requirements & GPU Compatibility
Chat
FastVLM 7B is a 7.8B-parameter open language model from Apple. It supports a context window of up to 32,768 tokens. At BF16 it needs about 15.95 GB of VRAM — see which GPUs and Macs can run it below.
1.6K downloads
269 likesSpecifications
- Publisher
- Apple
- Parameters
- 7.8B
- Architecture
- LlavaQwen2ForCausalLM
- Context Length
- 32,768 tokens
- Vocabulary Size
- 152,064
- Release Date
- 2025-08-25
- License
- apple-amlr
Get Started
HuggingFace
How Much VRAM Does FastVLM 7B Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| BF16est. | 16.00 | 15.9 GB | 17.7 GB | 15.53 GB | Brain floating point 16 — preferred for training |
est.= calculated VRAM estimate; no published GGUF file found for that quantization yet. Other rows are verified against real community uploads.
Which GPUs Can Run FastVLM 7B?
BF16 · 15.9 GBFastVLM 7B (BF16) requires 15.9 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 21+ GB is recommended. Using the full 33K context window can add up to 1.8 GB, bringing total usage to 17.7 GB. 26 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti, NVIDIA GeForce RTX 5080.
Runs great
— Plenty of headroom
Decent
— Enough VRAM, may be tightWhich Devices Can Run FastVLM 7B?
BF16 · 15.9 GB47 devices with unified memory can run FastVLM 7B, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, Mac Mini M4 (16 GB).
Runs great
— Plenty of headroomRelated Models
Frequently Asked Questions
- How much VRAM does FastVLM 7B need?
FastVLM 7B requires 15.9 GB of VRAM at BF16. Full 33K context adds up to 1.8 GB (17.7 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 7.8B × 16 bits ÷ 8 = 15.5 GB
KV Cache + Overhead ≈ 0.4 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 2.2 GB (at full 33K context)
VRAM usage by quantization
BF1615.9 GBBF16 + full context17.7 GB- Can I run FastVLM 7B on a Mac?
FastVLM 7B requires at least 15.9 GB at BF16, which exceeds the unified memory of most consumer Macs. You would need a Mac Studio or Mac Pro with a high-memory configuration.
- Can I run FastVLM 7B locally?
Yes — FastVLM 7B can run locally on consumer hardware. At BF16 quantization it needs 15.9 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is FastVLM 7B?
At BF16, FastVLM 7B can reach ~276 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~41 tok/s. Speed depends mainly on GPU memory bandwidth. Real-world results typically within ±20%.
tok/s = (bandwidth GB/s ÷ model GB) × efficiency
Example: NVIDIA B200 → 8000 ÷ 15.9 × 0.65 = ~326 tok/s
Estimated speed at BF16 (15.9 GB)
~326 tok/s~41 tok/s~326 tok/s~276 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of FastVLM 7B?
At BF16, the download is about 15.53 GB.
- Which GPUs can run FastVLM 7B?
26 consumer GPUs can run FastVLM 7B at BF16 (15.9 GB). Top options include AMD Radeon RX 7900 XTX, NVIDIA GeForce RTX 3090, NVIDIA GeForce RTX 3090 Ti, AMD Radeon RX 6800. 7 GPUs have plenty of headroom for comfortable inference.
- Which devices can run FastVLM 7B?
49 devices with unified memory can run FastVLM 7B at BF16 (15.9 GB), including AMD Ryzen AI 9 HX 370 (Strix Point) Laptop, ASUS Ascent GX10, Asus ROG Flow Z13 (2025, Ryzen AI Max+ 395, 128 GB), Beelink GTR9 Pro (Ryzen AI Max+ 395, 128 GB). Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.